Data Parallelism VS Model Parallelism in Distributed Deep Learning Training 分布式训练一(入门介绍) 深度学习中的分布式并行介绍
Data Parallelism示意图 数据并行可以提高训练效率,其过程如下: 将模型参数拷贝至各个显卡上,即上图中各个显卡都拥有相同的模型参数; 将采样的mini-batch数据均等拆分至各个显卡上; 各个显卡独立完成前向传播和反向传播,得到对应的梯度(此时,各个显卡上的梯度并不相同); 通过一次 AllReduce 操作,将各个显卡上的梯度进...
1. GPU与CPU集群上Model/Data Parallelism不存在本质上的区别，更多是存在工程细节上的区别。对这些工程...
Carlier, A Static Execution Model for Data Parallelism, Parallel Processing Letter, vol. 4, pp.367-378, Dec. 1994.C. Germain, F. Delaplace, and R. Carlier. A static execution model for data parallelism. LRITR-93-862. Submitted to Parallel Processing Letters ....
in model.parameters(): param.requires_grad = False # freeze the model - train adapters later if param.ndim == 1: # cast the small parameters (e.g. layernorm) to fp32 for stability param.data = param.data.to(torch.float32) model.gradient_checkpointing_enable() model.enable_input_...
Parallelism overviewIn the modern machine learning the various approaches to parallelism are used to:fit very large models onto limited hardware - e.g. t5-11b is 45GB in just model params significantly speed up training - finish training that would take a year in hoursWe...
Existing MoE systems support only expert, data, and model parallelism or a subset of them. This leads to three major limitations: i) They replicate the base model (part of the model without expert parameters) across data-parallel GPUs, resulting in wasted memory, (ii) They need model ...
billion parameter transformer language model: GPT-2 8B. The model was trained using nativePyTorchwith 8-way model parallelism and 64-way data parallelism on 512 GPUs. GPT-2 8B is thelargest Transformer-based language model ever trained, at 24x the size of BERT and 5.6x the size of GPT-2...
Performance microbenchmarks for pipeline parallelism In this section, we evaluated the computational performance of these pipeline-parallel schemes. This section does not use data parallelism, but we show results with both data and model parallelism later in this post. ...
Below are some common approaches used in Model Based Testing: Statecharts: An advanced form of finite state machines (FSMs) that supports complex transitions, parallelism, and hierarchical states. Often used to model reactive systems like embedded devices and user interfaces. Markov Models: Represent ...